In this paper, we study the tunable quantum neural network architecture in the quantum exact learning framework with access to a uniform quantum example oracle. We present an approach that uses amplitude amplification to correctly tune the network to the target concept. We applied our approach to the class of positive k-juntas and found that O(n22k) quantum examples are sufficient with experimental results seemingly showing that a tighter upper bound is possible.
@article{arxiv.2309.00561,
title = {Exact Learning with Tunable Quantum Neural Networks and a Quantum Example Oracle},
author = {Viet Pham Ngoc and Herbert Wiklicky},
journal= {arXiv preprint arXiv:2309.00561},
year = {2023}
}